Background: While managing patients with high-grade gliomas (HGG), predicting recurrence, or differentiating between pseudoprogression (radiation necrosis) and true tumour progression would be invaluable in improving overall prognosis. Characterizing small RNA (sRNA) expression profiles from plasma-derived extracellular vesicles (EVs) over the course of a patient’s treatments, may allow for patient-specific treatment modifications and improve outcomes. Methods: EVs were isolated using Vn96 capture from plasma obtained longitudinally from HGG patients perioperatively and with routine, follow-up surveillance imaging. sRNA was enriched from the EVs, upon which sequencing and unsupervised hierarchal clustering of sRNA signatures were completed. Expression profiles were grouped longitudinally with the clinical status of patients. Results: Cluster analysis of sequences from nine HGG patients, has revealed a sRNA signature that is able to distinguish between tumours showing evidence of progression and those remaining stable over time. Those samples obtained from patients where a clinical diagnosis of tumour progression or pseudoprogression were uncertain, were found to cluster into progression vs. stable signatures. Clinical follow up of these patients will reveal the predictive value of these identified clusters. Conclusions: These preliminary findings demonstrate the potential utility of small RNA profiling of EVs obtained from patients with high-grade gliomas as non-invasive biomarkers for recurrent/progressive disease or stability/pseudoprogression.